A Critical Stylistics Analysis of Sports Commentaries
Bibliographic record
Abstract
Drawing on Jeffries’ Critical Stylistics Analysis, the current study was done on the FIFA World Cup Qatar 2022 commentaries to reveal the discursive strategies used by the commentators that contribute to the ideological themes embedded in their commentaries. Six matches from the tournament were recorded and transcribed. The commentaries were then analysed using Jeffries' Critical Stylistics Analysis toolkits called textual-conceptual functions. Though not prominent, the findings reveal traces of ideologies of representation of races and religion found in the commentaries only by using six out ten Jeffries’ textual-conceptual functions toolkits. The current study helps sports commentators to comprehend how commentaries influence viewers’ perceptions of sports and the world around us. The current study adds to the literature on ideological frameworks to determine distinct frameworks employed in sports commentary. This could be useful for scholars interested in the relationship between language and ideology, as it could give them an excellent grasp of how these frameworks are employed to develop and maintain specific worldviews.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".